



'''simple docstring'''

from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


SCREAMING_SNAKE_CASE__     :						List[Any]	=  8






def 				a  (     UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    Optional[Any]=BITS			)       ->	Tuple:
		snake_case__	     =x.device

		snake_case__	     =(x * 255).int().clamp(0      ,					255			)

		snake_case__	     =2 ** torch.arange(bits - 1      ,					-1      ,					-1      ,					device=UpperCamelCase_			)
		snake_case__	     =rearrange(UpperCamelCase_      ,					'd -> d 1 1'			)
		snake_case__	     =rearrange(UpperCamelCase_      ,					'b c h w -> b c 1 h w'			)

		snake_case__	     =((x & mask) != 0).float()
		snake_case__	     =rearrange(UpperCamelCase_      ,					'b c d h w -> b (c d) h w'			)
		snake_case__	     =bits * 2 - 1
		return bits






def 				a  (     UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    List[str]=BITS			)       ->	Optional[Any]:
		snake_case__	     =x.device

		snake_case__	     =(x > 0).int()
		snake_case__	     =2 ** torch.arange(bits - 1      ,					-1      ,					-1      ,					device=UpperCamelCase_      ,					dtype=torch.intaa			)

		snake_case__	     =rearrange(UpperCamelCase_      ,					'd -> d 1 1'			)
		snake_case__	     =rearrange(UpperCamelCase_      ,					'b (c d) h w -> b c d h w'      ,					d=8			)
		snake_case__	     =reduce(x * mask      ,					'b c d h w -> b c h w'      ,					'sum'			)
		return (dec / 255).clamp(0.0      ,					1.0			)






def 				a  (     self    :    Dict      ,					UpperCamelCase_    :    torch.FloatTensor      ,					UpperCamelCase_    :    int      ,					UpperCamelCase_    :    torch.FloatTensor      ,					UpperCamelCase_    :    float = 0.0      ,					UpperCamelCase_    :    bool = True      ,					UpperCamelCase_    :    Any=None      ,					UpperCamelCase_    :    bool = True      ,					)       ->	Union[DDIMSchedulerOutput, Tuple]:
		if self.num_inference_steps is None:
				raise ValueError(
				    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'			)

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		snake_case__	     =timestep - self.config.num_train_timesteps // self.num_inference_steps

		# 2. compute alphas, betas
		snake_case__	     =self.alphas_cumprod[timestep]
		snake_case__	     =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

		snake_case__	     =1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

		# 4. Clip "predicted x_0"
		snake_case__	     =self.bit_scale
		if self.config.clip_sample:
				snake_case__	     =torch.clamp(UpperCamelCase_      ,					-scale      ,					UpperCamelCase_			)

		# 5. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		snake_case__	     =self._get_variance(UpperCamelCase_      ,					UpperCamelCase_			)
		snake_case__	     =eta * variance ** 0.5

		if use_clipped_model_output:
				# the model_output is always re-derived from the clipped x_0 in Glide
				snake_case__	     =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

		# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		snake_case__	     =(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

		# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		snake_case__	     =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if eta > 0:
				# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
				snake_case__	     =model_output.device if torch.is_tensor(UpperCamelCase_			) else 'cpu'
				snake_case__	     =torch.randn(model_output.shape      ,					dtype=model_output.dtype      ,					generator=UpperCamelCase_			).to(UpperCamelCase_			)
				snake_case__	     =self._get_variance(UpperCamelCase_      ,					UpperCamelCase_			) ** 0.5 * eta * noise

				snake_case__	     =prev_sample + variance

		if not return_dict:
				return (prev_sample,)

		return DDIMSchedulerOutput(prev_sample=UpperCamelCase_      ,					pred_original_sample=UpperCamelCase_			)






def 				a  (     self    :    Dict      ,					UpperCamelCase_    :    torch.FloatTensor      ,					UpperCamelCase_    :    int      ,					UpperCamelCase_    :    torch.FloatTensor      ,					UpperCamelCase_    :    List[str]="epsilon"      ,					UpperCamelCase_    :    Union[str, Any]=None      ,					UpperCamelCase_    :    bool = True      ,					)       ->	Union[DDPMSchedulerOutput, Tuple]:
		snake_case__	     =timestep

		if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
				snake_case__     ,	snake_case__	     =torch.split(UpperCamelCase_      ,					sample.shape[1]      ,					dim=1			)
		else:
				snake_case__	     =None

		# 1. compute alphas, betas
		snake_case__	     =self.alphas_cumprod[t]
		snake_case__	     =self.alphas_cumprod[t - 1] if t > 0 else self.one
		snake_case__	     =1 - alpha_prod_t
		snake_case__	     =1 - alpha_prod_t_prev

		# 2. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
		if prediction_type == "epsilon":
				snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
		elif prediction_type == "sample":
				snake_case__	     =model_output
		else:
				raise ValueError(f"""Unsupported prediction_type {prediction_type}."""			)

		# 3. Clip "predicted x_0"
		snake_case__	     =self.bit_scale
		if self.config.clip_sample:
				snake_case__	     =torch.clamp(UpperCamelCase_      ,					-scale      ,					UpperCamelCase_			)

		# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		snake_case__	     =(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
		snake_case__	     =self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

		# 5. Compute predicted previous sample µ_t
		# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
		snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

		# 6. Add noise
		snake_case__	     =0
		if t > 0:
				snake_case__	     =torch.randn(
				    model_output.size()      ,					dtype=model_output.dtype      ,					layout=model_output.layout      ,					generator=UpperCamelCase_			).to(model_output.device			)
				snake_case__	     =(self._get_variance(UpperCamelCase_      ,					predicted_variance=UpperCamelCase_			) ** 0.5) * noise

		snake_case__	     =pred_prev_sample + variance

		if not return_dict:
				return (pred_prev_sample,)

		return DDPMSchedulerOutput(prev_sample=UpperCamelCase_      ,					pred_original_sample=UpperCamelCase_			)


class 							a__( snake_case__     ):






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = 1.0					,   )							->							List[str]:
					super().__init__()
					snake_case__	     =bit_scale
					snake_case__	     =(
					    ddim_bit_scheduler_step if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else ddpm_bit_scheduler_step
					)

					self.register_modules(unet=_UpperCAmelCase					,   scheduler=_UpperCAmelCase						)






			@torch.no_grad()
			def __call__(     self					,   _UpperCAmelCase = 256					,   _UpperCAmelCase = 256					,   _UpperCAmelCase = 50					,   _UpperCAmelCase = None					,   _UpperCAmelCase = 1					,   _UpperCAmelCase = "pil"					,   _UpperCAmelCase = True					,   **_UpperCAmelCase					,   )							->							Union[Tuple, ImagePipelineOutput]:
					snake_case__	     =torch.randn(
					    (batch_size, self.unet.config.in_channels, height, width)					,   generator=_UpperCAmelCase					,   )
					snake_case__	     =decimal_to_bits(_UpperCAmelCase						) * self.bit_scale
					snake_case__	     =latents.to(self.device						)

					self.scheduler.set_timesteps(_UpperCAmelCase						)

					for t in self.progress_bar(self.scheduler.timesteps						):
							# predict the noise residual
							snake_case__	     =self.unet(_UpperCAmelCase					,   _UpperCAmelCase						).sample

							# compute the previous noisy sample x_t -> x_t-1
							snake_case__	     =self.scheduler.step(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						).prev_sample

					snake_case__	     =bits_to_decimal(_UpperCAmelCase						)

					if output_type == "pil":
							snake_case__	     =self.numpy_to_pil(_UpperCAmelCase						)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=_UpperCAmelCase						)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




